generative-models

Train with MNIST and generate hand-written numbers with generative models including VAE, DCGAN and RealNVP.

Generative Models

The following generative models are included, with configs in config.ini:

VAE (Variational Auto-Encoder) in vae.py
DCGAN (Deep Convolutional Generative Adversarial Networks) in dcgan.py
RealNVP (Real-valued Non-Volume Preserving) in realnvp.py

Prerequisites

Install the required packages by:

pip install -r requirements.txt

Data: MNIST

Sample

Dataset

MNIST	Train	Dev	Test
Amount	48000	12000	10000

VAE

Model

Command Line

bash run_vae.sh

Log with Tensorboard

tensorboard --logdir=log/vae/

Loss

Train Batch Loss	Train Epoch Loss

Dev Loss with best epoch 62	Test Loss
	102.0952

Reconstructed Result

Epoch 1	Epoch 10	Epoch 20

Epoch 30	Epoch 40	Epoch 50

Epoch 64	Test Result

Generated Result

Epoch 1	Epoch 10	Epoch 20

Epoch 30	Epoch 40	Epoch 50

Epoch 64

Reference

Auto-Encoding Variational Bayes

Link

VAE in pytorch/examples

DCGAN

Model

Generator(
  (main): Sequential(
    (0): ConvTranspose2d(100, 512, kernel_size=(4, 4), stride=(1, 1), bias=False)
    (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU(inplace)
    (3): ConvTranspose2d(512, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (5): ReLU(inplace)
    (6): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (7): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (8): ReLU(inplace)
    (9): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (10): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (11): ReLU(inplace)
    (12): ConvTranspose2d(64, 1, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (13): Tanh()
  )
)

Discriminator(
  (main): Sequential(
    (0): Conv2d(1, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (1): LeakyReLU(negative_slope=0.2, inplace)
    (2): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (4): LeakyReLU(negative_slope=0.2, inplace)
    (5): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (6): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (7): LeakyReLU(negative_slope=0.2, inplace)
    (8): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (9): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (10): LeakyReLU(negative_slope=0.2, inplace)
    (11): Conv2d(512, 1, kernel_size=(4, 4), stride=(1, 1), bias=False)
    (12): Sigmoid()
  )
)